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Peer prediction mechanisms are often adopted to elicit truthful contributionsfrom crowd workers when no ground-truth verification is available. Recently,mechanisms of this type have been developed to incentivize effort exertion, inaddition to truthful elicitation. In this paper, we study a sequential peerprediction problem where a data requester wants to dynamically determine thereward level to optimize the trade-off between the quality of informationelicited from workers and the total expected payment. In this problem, workershave homogeneous expertise and heterogeneous cost for exerting effort, bothunknown to the requester. We propose a sequential posted-price mechanism todynamically learn the optimal reward level from workers' contributions and toincentivize effort exertion and truthful reporting. We show that (1) in ourmechanism, workers exerting effort according to a non-degenerate thresholdpolicy and then reporting truthfully is an equilibrium that returns highestutility for each worker, and (2) The regret of our learning mechanism w.r.t.offering the optimal reward (price) is upper bounded by $\tilde{O}(T^{3/4})$where $T$ is the learning horizon. We further show the power of our learningapproach when the reports of workers do not necessarily follow thegame-theoretic equilibrium.

Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices